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Title: SPECTRUM ALLOCATIONS ALGORITHMS IN WIRELESS NETWORKS
Creator: Xu, Ping
Date: 2011-04-26, 2011-05
Description: All wireless devices rely on access to the radio frequency spectrum, which has been chronically regulated by static spectrum allocation...
Show moreAll wireless devices rely on access to the radio frequency spectrum, which has been chronically regulated by static spectrum allocation policies. With the recent fast growing of spectrum-based services and devices, the remaining spectrum available for future wireless services is being exhausted, known as the spectrum scarcity problem. The current fixed spectrum allocation scheme leads to significant spectrum white spaces (including spectral, temporal, and geographic), where many allocated spectrum blocks are used only in certain geographical areas and/or in brief periods of time. In this work, we design and analyze variant spectrum allocation algorithms for better spectrum utilization and study some fundamental performance bounds for networks with opportunistic spectrum utilization. We first propose spectrum allocation algorithms for offline model, in which all spectrum requests are known when allocation decision is made. Then we also addresse the problems in online model, where allocation decision should be made when only a few spectrum requests are known. In the online model, we focus on two different cases. The first one assumes no statistic of future spectrum requests are known, and the second one assumes some statistic is known or can be learned. For all these models, we design efficient spectrum allocation methods and analytically prove most of them are asymptotically optimal. Our extensive simulation results also verify our theoretical conclusion.
Ph.D. in Computer Science, May 2011
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Title: EFFECTIVENESS OF CLEANlNG REGIMENS FOR REMOVlNG MILK RESIDUE FROM A PILOT -SCALE HTST PROCESSING LINE
Creator: Du, Qian
Date: 2011-11-30, 2011-12
Description: Undeclared allergens can be introduced into foods due to cross-contact during manufacture. Effective cleaning is essential for preventing...
Show moreUndeclared allergens can be introduced into foods due to cross-contact during manufacture. Effective cleaning is essential for preventing allergen cross-contact on shared processing lines. The objectives of this project were to evaluate the effectiveness of cleaning treatments on removing milk residue from a pilot-scale HTST processing line, and measure the levels of milk transferred into juice processed on an inadequately cleaned processing line. Nonfat milk was processed (81ºC,17 sec) on an HTST processing line followed by several cleaning regimens including 1) a 15 min water flush, 2) a 15 min water flush + a 60 min wash using full-strength chlorinated alkaline detergent (CAD) at 81ºC and a flow rate of 55-60 gal/h , 3) a 15 min water flush + a 60 min wash with ¼-strength CAD at 81ºC and flow rate of 55-60 gal/h, 4) a 15 min water flush + a 15 min full-strength CAD at 81ºC and flow rate of 55-60 gal/h, 5) a 15 min water flush + a 60 min full-strength CAD at 70ºC and flow rate of 55-60 gal/h, 6) a 15 min water flush + a 60 min full-strength CAD at 81ºC and flow rate of 27.7 gal/h, 7) a 15 min water flush + a 60 min full-strength CAD containing 1% milk at 81ºC and flow rate of 55-60 gal/h, 8) a a full cleaning cycle (15 min water flush + 60 min full-strength CAD at 81 ºC + 15 min water flush + 30 min acid detergent at 70 ºC + 15 min water flush + 15 min flush with 200 ppm sodium hypochlorite sanitizer at room temperature, flow rate of 55-60 gal/h). After each cleaning treatment, simulated apple juice was processed on the same processing line, collected and tested for presence of milk using a quantitative ELISA. The adequacy of the cleaning treatments was also assessed by determining the absence/presence of milk residue in sampling ports located in the processing line with ATP swabs, protein swabs and a milk-specific lateral flow kit. All clean treatments and analyses were done in triplicate. Milk was detected at levels of 59-150 μg/mL (ppm) in simulated apple juice processed on the HTST line after a 15 min water flush. No milk was detected in juice processed on the line cleaned with full-strength CAD or a full cleaning cycle. Lower milk levels in apple juice were detected with some of the intermediate cleaning regimens (¼-strength CAD, shorter cleaning time, reduced temperature of CAD, lower flow rate 27.7 gal/h of CAD cleaning and CAD with 1% milk). Swabs of sampling ports revealed the presence of milk/protein residue and high ATP levels after the water flush. Cleaning treatments using full-strength CAD reduced ATP levels and resulted in the no detectable of protein/milk residue in most sampling ports and in simulated apple juice. Reuse of CAD containing high levels of milk may result in milk cross-contact into juice subsequently processed on the milk processing line. This work illustrates the importance of validated cleaning procedures to prevent allergen cross-contact on shared processing lines.
M.S. in Food Safety and Technology, December 2011
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Title: FIBRONECTIN INFLUENCES THE RATE OF ASSEMBLY AND STRUCTURAL CHARACTERISTICS OF THE FIBRIN MATRIX AND A MAP OF LYSINE PEGYLATION SITES IN FIBRONECTIN
Creator: Ramanathan, Anand
Date: 2015, 2015-07
Description: Fibronectin serves multiple roles during tissue formation and wound healing, functioning through interactions with cells and extracellular...
Show moreFibronectin serves multiple roles during tissue formation and wound healing, functioning through interactions with cells and extracellular molecules. The overall objective of my research was to investigate fibronectin biochemistry on responses associated with wound healing. My approach was to engineer relevant in vitro models highlighting fibronectin functionality in tissues and link this work to more complex wound healing systems. My research goals were accomplished through the following three specific aims: (1) Determine the role of fibronectin on the kinetics of formation and structure of a fibrin-fibronectin matrix, (2) Determine the effect of protease on the activity of fibronectin in decellularized extracellular matrices and (3) Map the sites of polyethylene glycol conjugation or PEGylation to lysine residues in fibronectin. Aim 1: I demonstrated that fibronectin increased the initial rate of fibrin matrix formation and altered the fibrin matrix structure. These findings are novel because they link results from light absorbance studies to microcopy analyses and demonstrate the influence of fibronectin on fibrin matrix structural characteristics. Aim 2: I demonstrated a link between fibronectin proteolysis and reduced cell adhesion in decellularized extracellular matrices. This study demonstrates the susceptibility of fibronectin to proteolysis in the extracellular matrix and the resulting loss of matrix functionality, placing weight on bioengineering strategies to stabilize fibronectin against proteolysis. Aim 3: I examined proteolytic fragments of native and PEGylated fibronectin to map fibronectin lysine residues that are conjugated PEG. From four key chymotryptic fragments that span fibronectin and are recognized by specific monoclonal antibodies, I provide a map of lysine PEGylation sites for fibronectin. Moreover, I show that lysine PEGylation of fibronectin occurs asymmetrically on the dimer arms. Knowledge of the lysine PEGylation sites can be used to plan future experiments for investigating fibronectin biochemical interactions in complex in vitro and in vivo models. In accomplishing these specific aims, I identified key biomolecular mechanisms involving fibronectin and created relevant in vitro models to study these interactions. The work detailed in this thesis lays the foundation for future experiments to investigate fibronectin functionality and develop therapeutic strategies targeting fibronectin biochemistry in tissue development.
Ph.D. in Chemical Engineering, July 2015
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Title: EFFECTS OF FUEL PROPERTIES ON THE COMBUSTION PROCESS OF AN ADVANCED DIESEL ENGINE
Creator: Ramos Silva, Cedric Zacarias
Date: 2015, 2015-12
Description: Internal combustion engines are encountered in our everyday lives in passenger cars and heavy-duty vehicles such as trucks and buses. While...
Show moreInternal combustion engines are encountered in our everyday lives in passenger cars and heavy-duty vehicles such as trucks and buses. While conventional compression ignition engines burn diesel fuel with an oxidizer (generally air) in a combustion chamber, much recent research has focused on improving the efficiency of combustion and reducing vehicle pollutant output through the usage of fuels with properties which differ from those of diesel fuel. In particular, this study focuses on a dual fuel engine in which two fuels (usually gasoline or diesel fuel mixed with an alternate fuel) are separately injected and combusted. Results from an Argonne National Laboratory test cell utilizing a 13 Liter (L) heavy duty dual fuel engine running in a combustion mode known as Reactivity Controlled Compression Ignition (RCCI) were leveraged in this work. In a RCCI engine, two fuels of different reactivities (low reactivity and high reactivity) are used in order to control in-cylinder fuel reactivity and allow for the optimization of combustion phasing and duration. In addition, RCCI combustion has been shown to produce low amounts of nitrogen oxides (NOx) as well as particulate matter (PM) emissions which may eliminate the need for expensive after-treatment systems. The combustion shaping capabilities and low emissions of RCCI dual fuel engines enable reductions in heat transfer losses and as such the increase of fuel efficiency. In order to understand the dynamics of such engines, a detailed simulation model of a RCCI dual fuel engine was constructed and developed using the Gamma Technologies (GT) simulation suite in particular GT-POWER and GT-SUITE. Modeling of the complex gas exchange process as well as the combustion process of the 13L RCCI dual fuel engine were both undertaken. This model was then leveraged to examine the effect of fuel properties on the combustion process using GT simulation suite. Experimental data from the 13L engine at Argonne was used to validate the models of the gas exchange and combustion processes. For the gas exchange process as well as the combustion process, the results from the simulation model fairly accurately match the experimental data from the Argonne engine. To achieve RCCI, the engine is equipped with a complex air handling system which includes two turbochargers as well as exhaust gas recirculation (EGR). To ensure that the gas exchange process was accurately captured, the experimental intake pressure, EGR fraction (EGR mass flow rate divided by the sum of EGR mass flow rate and air mass flow), fresh airflow rate, maximum in-cylinder pressure, IMEP and exhaust pressure were compared with the simulation results given by GT-POWER and GT-POST. By modeling the engine components in GT-POWER and adding additional control algorithms, the previously mentioned parameters predictions were within 10% of the engine data. The combustion process was modeled using a Direct-Injection Jet (DI-Jet) combustion model. The DI-Jet model is a predictive combustion model which predicts the burn rate, combustion rate and NOx emissions. This model was calibrated by comparing the experimental and simulation heat release curves. Particular attention is given to accurately capturing the start of combustion and ignition delay period because they affect the combustion process the most.
M.S. in Mechanical and Aerospace Engineering, December 2015
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Title: MOTIVES FOR SOCIAL MEDIA USE AMONG PRACTITIONERS AT NONPROFIT ORGANIZATIONS
Creator: Roback, Andrew J.
Date: 2017, 2017-05
Description: I used the motivation concept from activity theory to derive a fundamental notion of why workers at nonprofit organizations (NPOs) use social...
Show moreI used the motivation concept from activity theory to derive a fundamental notion of why workers at nonprofit organizations (NPOs) use social media sites. This study rejects the notion that practitioners are not taking full advantage of social media sites by not using every available feature and engaging in dialogic communication. Existing work relies too extensively on the dialogic model of communication and frequently focuses on only top-tier NPOs, ignoring the context in which smaller NPOs operate and producing recommendations that are of little practical value. To investigate this issue, I reviewed existing best practices as portrayed in NPO social media strategy guides, and used the principles of activity theory to survey practitioners at human services NPOs in Chicago. I collected data on user motivation for using Facebook and Twitter by asking users to review past posts on these sites and describe their purpose in posting this information. Using this information, I trained an automated text classifier to classify a large corpus of posts based on four types of motivations: soliciting, promoting, sharing, and credit-giving. This dissertation builds off recent studies that question existing wisdom on “effective” use of social media by NPOs and argues for an expanded consideration of user agency and intent when using social media.
Ph.D. in Technical Communication, May 2017
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Title: DEVELOPING METHODS TO IDENTIFY SURROGATES FOR ESCHERICHIA COLI O157:H7 IN VALIDATION OF FRESH PRODUCE WASHING PROCESSES
Creator: Rolfe, Catherine
Date: 2016, 2016-07
Description: Cross-contamination during fresh produce washing is commonly prevented using chlorine treatment. Surrogate microorganisms have been widely...
Show moreCross-contamination during fresh produce washing is commonly prevented using chlorine treatment. Surrogate microorganisms have been widely used in process validation and to assess microbial cross-contamination. Fresh produce washing incorporates physical, chemical, biological and kinetic factors which create an intricate process for which little is known regarding surrogate selection. The purpose of this study was to identify the important elements relevant to produce washing processes and identify methods that will be used in surrogate selection. The behavior of three (3) non-pathogenic microorganisms (generic E. coli Nissle 1917 EcN, Pediococcus pentosaceus and lettuce isolate 813-F1) were examined in comparison to E. coli O157:H7 based on phenotypic similarities. Chlorine inactivation kinetics of E. coli O157:H7 and the non-pathogenic strains were evaluated with varying pH levels (6.5 and 8.0) and exposure times (3-30 seconds). Detachment of leaf-bound E. coli O157:H7 and non-pathogenic strains at different inoculation levels (approximately 2 and 6 log CFU/mL) and drying conditions (aging time, temperature) in wash water was examined. Chlorine inactivation at pH 6.5 resulted in a range of viability corresponding to E. coli O157:H7 and the non-pathogenic strains; demonstrating a sharp inactivation curve for E. coli O157:H7, EcN and P. pentosaceus. Whereas, inactivation at pH 8.0 allowed more survival relating to exposure time for all microorganisms. Detachment from inoculated leaves at 2 and 6 log CFU/mL inoculation showed steady survival levels in wash water at 0 ppm and lower survival at 1 ppm for all strains excluding 813-F1; 813-F1 was consistently less chlorine-sensitive in chlorine inactivation assays and more cross-contamination to wash water was observed for this strain. Aging time of inoculated bacteria on leaves was not seen to have remarkable effects on bacterial transfer during washing. These results suggest assay methods of chlorine inactivation at pH 6.5 and detachment with 6 log CFU/mL initial inoculation may be useful in selecting appropriate surrogates for fresh produce washing.
M.S. in Food Safety and Technology, July 2016
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Title: AUTOMATED SLICING METHODS FOR LARGE EVENT TRACES
Creator: Smith, Raymond D.
Date: 2012-05-02, 2012-05
Description: Many long-running computer systems record events as they execute, resulting in a dynamic record of system behavior. In large systems, the...
Show moreMany long-running computer systems record events as they execute, resulting in a dynamic record of system behavior. In large systems, the event trace may contain thousands of entries and when faced with a problem for analysis, programmers must sort through many disparate events to find those that are related to the system behavior under study and eliminate those that are not. In this research we investigated automatic reduction of event traces to reduce the volume of events and assist in analysis of behavior of large systems. Our approach was to adapt the techniques used in program slicing to compute event trace slices as a means of reduction. Two methods for slicing of event traces were proposed and investigated. The Event Dependence Based method (EDB) uses information available in the event trace to identify dependencies between events and to compute an event trace slice that meets a slicing criterion. The Model Dependence Based method (MDB) incorporates the use of an executable state-based system model to achieve further reduction of traces. The method identifies model-based dependences in the trace to compute trace slices. An experimental study was performed on simulated systems, representative of state-based software systems present in industry to analyze and compare the EDB and MDB slicing methods. Both methods provided significant reduction of event traces, particularly for systems with a low degree of sharing and interaction among resources. However, the MDB method significantly outperformed the EDB method for systems with a high degree of resource sharing.
Ph.D. in Computer Science, May 2012
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Title: RENEWABLE ENERGY IN MICROGRID: A STOCHASTIC OPTIMIZATION APPROACH
Creator: Jin, Hongwei
Date: 2014, 2014-12
Description: A Microgrid is a group of interconnected loads and distributed energy resources within clearly defined electrical boundaries that acts as a...
Show moreA Microgrid is a group of interconnected loads and distributed energy resources within clearly defined electrical boundaries that acts as a single controllable entity with respect to the grid and that connects and disconnects from such grid to enable it to operate in both grid-connected or island mode. The optimized energy scheduling problem is significant to both utilities and community consumers. In this thesis, I present an approach by analyzing the historical weather data and renewable energy data, and build a two-stage stochastic program with a long term view towards minimizing costs. The underlying stochastic process that generates uncertainty in demand and supply in power network is the local weather, thus understanding solar radiation as a function of weather is significant to us. First, two simple methods, which are majority rule and flexible time selection, are proposed with the purpose of handling noisy raw data and giving a relatively precise prediction of renewable energy consumption and overall energy demands. Then, I implement a deterministic strategy, a two-stage stochastic program and a repeated stochastic program using AMPL, a mathematical modeling language. Every stochastic program is defined as based on 42 scenarios from the weather conditions. In the final step, I solve the model using CPLEX and compare optimal solutions based on a year-long Monte Carlo simulation. Ignoring installation and maintenance costs, the Microgrid can make some profit by an optimized control based on our modeling approach utilizing the stochastic optimization paradigm. Although there are only slight differences between three models, the repeated two-stage stochastic model gives the best long-term results.
M.S. in Applied Mathematics, December 2014
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Title: GENDER DIFFERENCES IN POSTCONCUSSIVE SYMPTOMS OF SPORT-RELATED CONCUSSIONS IN CHILDREN AND ADOLESCENTS
Creator: Gretencord Roy, Ashley Aline
Date: 2016, 2016-07
Description: Current research on concussions indicates that both younger age and female gender are associated with a greater number of symptoms and a...
Show moreCurrent research on concussions indicates that both younger age and female gender are associated with a greater number of symptoms and a lengthier postconcussive recovery time. The aim of this research was to examine postconcussive symptoms (PCS) resulting from a sports-related concussion in both male and female children/adolescents. Data was collected using neuropsychology measures (Auditory Consonant Trigrams Test, Conners' Continuous Performance Test-2nd edition, Immediate Post-Concussion Assessment and Cognitive Testing, Woodcock Johnson Tests of Achievement- Third Edition, and Behavior Assessment System for Children-2nd edition) and a neurological evaluation. Participants included 132 children/adolescents (10-18 years) who had sustained a sports-related concussion. Results indicated evidence of subtle, but clinically significant, impairments in executive functioning. This was particularly true for those with a premorbid attention, learning, and/or mood disorder. In addition, a history of previous concussions was associated with a higher number of reported cognitive PCS. Hierarchical regression analyses were conducted for each of the dependent measures. As predicted, female gender was associated with increased executive dysfunction and a higher report of cognitive and emotional/behavioral PCS. Contrary to hypotheses, younger age was associated with less executive dysfunction and fewer reported cognitive PCS. No interaction between age and gender was identified. Implications of the findings are discussed.
Ph.D. in Psychology, July 2016
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Title: OPTIMAL TRANSMISSION SWITCHING AND SECURITY-CONSTRAINED UNIT COMMITMENT CONSIDERING DEMAND-SIDE PARTICIPATION
Creator: Ma, Ruicheng
Date: 2015, 2015-05
Description: Transmission topology is traditionally considered as fixed elements in electrical system. Transmission line states used to be presumably set...
Show moreTransmission topology is traditionally considered as fixed elements in electrical system. Transmission line states used to be presumably set as closed for the whole system, or sometimes open for security check purposes. In the development of a smart grid, however, the optimization of the use of transmission has been proposed as an advanced transmission technology. Optimal transmission switching (OTS) is a straightforward method to enhance grid controllability: to mitigate transmission violation, re-dispatch power generation, and meet changing demand with existing infrastructure. Previous papers have shown that co-optimization of generation and transmission problem will improve the economic performance. This thesis provides the formulation and solution methodology for applying OTS in day-ahead security-constrained unit commitment (SCUC) scheduling. Base case and contingency case are examined to ensure the feasibility of the solution. The OTS applications also consider the demand-side participation such as demand response (DR) and renewable energy. The results are presented based on a modified 6-bus system.
M.S. in Electrical Engineering, May 2015
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Title: BLAME, COPING, AND PYCHOSOCIAL OUTCOMES IN CAREGIVERS OF FAMILY MEMBERS WITH ACQUIRED BRAIN INJURY
Creator: Dedios-stern, Samantha L.
Date: 2015, 2015-05
Description: Acquired brain injury (ABI) is associated with many physical and psychiatric conditions. Oftentimes, the individual’s family members are...
Show moreAcquired brain injury (ABI) is associated with many physical and psychiatric conditions. Oftentimes, the individual’s family members are responsible for providing long-term care, leaving caregivers vulnerable to negative effects of caregiving including stress, physical, and psychological problems. Attribution theory suggests that when individuals experience distress, they may generate causal explanations for their circumstances by attributing blame regarding why the event happened. Frequently, blame attributions involve identifying the problem as being within another person. The objective of this study was to investigate caregiver coping strategies as possible mediators between caregiver family member blame and caregiver psychosocial outcomes among caregivers of individuals with ABI. Caregivers of individuals with ABI (n = 94) completed a brief online survey of self-report measures regarding coping (emotion-focused, problemfocused, and dysfunctional strategies), blame (direct, indirect, and preoccupation with blame), depressive symptoms, and quality of life (QOL). Bootstrapping mediation analyses were then conducted to investigate the mediating role of caregiver coping strategy between blame attributions, and either depressive symptoms or QOL. Results demonstrated that the use of more dysfunctional coping strategies significantly mediated the relationship between indirectly blaming one’s family member for their injury and subsequent depressive symptoms and QOL. Furthermore, using more dysfunctional coping strategies also significantly mediated the relationship between preoccupation with blame and depressive symptoms. Implications for intervention and future research are discussed.
M.S. in Psychology, May 2015
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Title: TOWARD THE AUTOMATIC ORGANIZATION AND COMPREHENSION OF SOCIAL NETWORK COMMUNICATION
Creator: Platt, Alana
Date: 2013, 2013-05
Description: Social networking sites are radically transforming the way we communicate and relate to each other. They facilitate timely information...
Show moreSocial networking sites are radically transforming the way we communicate and relate to each other. They facilitate timely information exchange and give us unprecedented access to numerous sources of information on a myriad of topics. Although the information is available, there are a number of challenges that inhibit utilization of this information: Social Networks have a great volume of messages that the user must sift through to find relevant ones, messages are frequently repetitive, the information is not organized topically, and there is little context information. The information consumer (user) must take on many of the tasks traditionally performed by the information producer to get a “big picture” understanding of the topic. This thesis introduces a framework for an automated information gathering and organization system to facilitate the information consumer’s comprehension of a given topic. The framework addresses two primary components: the user interface for the system and identification of sub-topics. The framework was implemented as a research platform designed to bring these two components together and support future research in the domain.
PH.D in Computer Science, May 2013
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Title: ADVANCED BASE DRIVERS FOR SILICON CARBIDE BJTs
Creator: Pozo Arribas, Alejandro
Date: 2017, 2017-07
Description: This thesis focuses on the optimization of base drivers for SiC BJTs and presents a novel driver topology that targets minimum power...
Show moreThis thesis focuses on the optimization of base drivers for SiC BJTs and presents a novel driver topology that targets minimum power consumption. SiC BJTs have been studied for over a decade, during which time, they have been proven to have superior performance than Si IGBTs and even other normally-off SiC devices such as MOSFETs. Despite this, SiC BJTs are the least popular among the family of SiC power switches. As current controlled devices, BJTs require a continuous sup- ply of current through the base during the on-time. And, even though current gains over 100 have been reported, the base current required translates into a considerable amount power consumed by its driver, compared to its competitors. This power can affect the overall efficiency of a converter if the driver circuit is not designed properly. Since, the driver represents a key system for the success of SiC BJTs as power semiconductor devices, this thesis conducts a comprehensive evaluation of previous solutions and an analysis of the driver power losses to identify the optimal driver configuration. As a conclusion of this study, a novel topology is proposed, designed and built for its latter validation through experimental tests. The proposed solution allows the replacement of a SiC MOSFET or Si IGBT and driver with a SiC BJT and driver without the need of a current sensor or a dedicated DSP/FPGA. The driver power consumption is minimized with a proportional base current design based on a MHz synchronous buck converter operating as a Class D amplifier. This switched mode power amplifier uses a reference signal to provide a voltage that causes a base current proportional to the instantaneous collector current. The reference signal is generated with a high bandwidth sensor that measures the instantaneous voltage drop across the BJT (vCE) during the on-time. Hence, current sensors are avoided. Different alternatives for a voltage sensor are discussed and analyzed through simulations and experimental results. Moreover, the use of vCE to estimate the instantaneous collector current makes the proposed driver a temperature-sensitive design. For the first time, a proportional base current driver generates a base current proportional to the instantaneous collector current taking into account the effect of temperature on the DC current gain. Moreover, all this is achieved with solely analog electronics in a standalone solution. A 1.5kW Boost converter was built to validate the proposed driver under different collector currents and operating temperatures. In order to show the performance improvement offered by the proposed solution, the same Boost converter was operated with a commercial base current driver. This exercise showed a reduction of the driver power consumption by up to a factor of 4 without affecting the efficiency of the Boost converter. The switching behavior of a SiC BJT operated with the proposed driver and some of its limitations are discussed. These have, in fact, motivated additional research to develop efficient, isolated MHz regulators for faster operating frequencies of the SiC BJT. In addition, a new over-current protection integrated into the proposed driver is suggested and tested with interrupt times of less than 500ns for a collector current of 50A.
Ph.D. in Electrical Engineering, July 2017
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Title: A NOVEL HYDRO-GENERATOR BASED ENERGY STORAGE CONCEPT FOR MICROGRID APPLICATIONS
Creator: Gu, Ran
Date: 2012-04-26, 2012-05
Description: The solution to deal with the current non-renewable energy crisis, global warming, pollution and green gas emission is to reduce fossil fuels...
Show moreThe solution to deal with the current non-renewable energy crisis, global warming, pollution and green gas emission is to reduce fossil fuels usage and increase use of renewable sources of energy with minimal environmental impact. Solar energy and wind energy have gained significant popularity as natural resources across the world. However, these renewable energies bring new challenges to the control of power systems and distributed generation since they depend on natural elements that can be unpredictable and intermittent. One way to address the intermittency of these resources is to transfer energy in to an energy storage device. Historically, a battery has been viewed as one of the primary energy storage devices. Even though some battery can have high efficiency, they can be limited by the size and volume required to store a large amount of energy. In addition, they can also cause environmental pollution owing to the chemicals used and tend to have a high cost and short life. A novel hybrid energy storage system, which comprises of an integrated hydroelectric-compressed air solution have been proposed in this thesis. Three potential configurations have been outlined, where energy is provided by wind and solar energy. To extract maximum power from wind, solar and water, maximum power point tracking (MPPT) techniques for both renewable sources have been proposed. For researching the interaction between charging and discharging elements, extensive simulations have been conducted using Matlab/ Simulink.
M.S. in Electrical Engineering, May 2012
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Title: IMPROVED MAXIMUM LIKELIHOOD ESTIMATION FOR GENERALIZED BASS MODEL
Creator: Razo, Martha
Date: 2017, 2017-05
Description: Today, businesses operate in an interconnected global economy, in which innovation happens on a moment to moment basis. Statistical predictive...
Show moreToday, businesses operate in an interconnected global economy, in which innovation happens on a moment to moment basis. Statistical predictive modeling in marketing and development is emerging as a crucial component to the success of small companies and large corporations alike. The goal of this paper is to analyze the Bass model as it pertains to sales of the Chevy Volt. The Bass model has been shown to be a useful tool for forecasting the sales of new products as they become available in the marketplace, but what are the model's limitations? The widely studied Bass model produces computational problems when we evaluate the model for a larger set data which extends to modified models constructed from the original Bass model. Kijek in [14] and Srinivasan and Mason in [16] alert us of the shortcomings of the Maximum Likelihood approach of solving the Bass model which extends to solving the Generalized Bass model, but these authors limit us to a vague listing without a close analysis. In this paper we present the issues of estimating the Bass model parameters when using the Maximum Likelihood approach. Furthermore, we introduce an improved generalized model which takes into account the shortcomings of the Bass model and our proposed approach of overcoming these. We will illustrate the limitations of the Bass model when using a large data set from the Chevy Volt car data published by Inside EVs[12]. Careful analysis of the Bass model and its current modifications provides a rich tool that has potential in changing the future of a company when introducing new products to the market.
M.S. in Applied Mathematics, May 2017
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Title: ESTIMATION OF THERMAL STATE OF CHARGE FOR PCC BASED LITHIUM-ION BATTERY PACKS
Creator: Salameh, Mohamad
Date: 2016, 2016-07
Description: With continuing efforts to improve energy and power density of Li-ion batteries, heat generation and thermal safety remain critical barriers...
Show moreWith continuing efforts to improve energy and power density of Li-ion batteries, heat generation and thermal safety remain critical barriers to commercial success. Energy conversion in a battery is an exothermic process. Whenever the temperature of lithium-ion batteries increases, there can be direct consequences-reduced calendar and cycle life and higher risk of a battery re or explosion. Conventional approaches to prevent overheating use active thermal management systems, such as air conditioning or liquid cooling. However, these systems can be costly, bulky, and consume energy during operation. In addition they o er no overheat protection while the application or the vehicle is powered down. Phase change material composites (PCC) can be employed to rapidly absorb heat from the battery and distribute it, thereby enabling lightweight and compact packs with extended cycle-life and safety. This thesis proposes an online temperature estimation technique for a novel intelligent battery thermal management to actively monitor thermal mass of the phase change material. Such a system will not only enable avoidance of thermal issues, but will extend life of the battery pack by optimally selecting the operating point of the Energy Storage System. It can also be used to predict when active cooling should be employed just before the battery exits the phase change temperature plateau, to ensure latent heat absorption is spread across the entire drive cycle.
M.S. in Electrical Engineering, July 2016
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Title: APPLICATION OF THE FEAR-AVOIDANCE MODEL OF CHRONIC PAIN TO UNDERSTAND NEUROCOGNITIVE AND BEHAVIORAL FACTORS THAT CONTRIBUTE TO FUNCTIONAL IMPAIRMENT AND DEPRESSION IN ADULTS WITH SICKLE CELL DISEASE
Creator: Piper, Lauren E.
Date: 2017, 2017-07
Description: Acute and chronic pain in sickle cell disease (SCD) are associated with functional impairment and depressive symptoms. Given the suboptimal...
Show moreAcute and chronic pain in sickle cell disease (SCD) are associated with functional impairment and depressive symptoms. Given the suboptimal management of pain in SCD and serious health risks associated with current treatment methods for pain, there is a need to identify factors associated with pain that impact functional outcomes and depression. The fear-avoidance (FA) model of chronic pain has been examined in other chronic pain populations as a means to understand how pain-related cognitive and behavioral factors contribute to functional impairment and depression, but has not been applied in individuals with SCD. The purpose of the present study was to apply the FA model of chronic pain to adults with SCD via mediation analyses. Additionally, mental flexibility was examined as a possible moderator in the FA model. Results demonstrated that pain catastrophizing mediated the relationship between pain severity and pain-related fear. No other mediators within the model were identified. Additionally, results did not demonstrate that mental flexibility moderated the relationship between pain severity and pain catastrophizing. Post-hoc exploratory analyses demonstrated that pain catastrophizing and pain-related fear significantly predicted functional impairment and depression, respectively, above and beyond pain severity. Overall, results suggest that the FA model of chronic pain does not apply to individuals with SCD and the predictive roles that pain catastrophizing and pain-related fear play in functional impairment and depression are not consistent with results in other chronic pain populations. Further studies are needed to identify factors that explain the relationship between pain, functional impairment, and depression so that these factors may be targeted for intervention as a means to improve pain, mood, and functional independence.
Ph.D. in Psychology, July 2017
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Title: Deep Learning and Model Predictive Methods for the Control of Fuel-Flexible Compression Ignition Engines
Creator: Peng, Qian
Date: 2022
Description: Compression ignited diesel engines are widely used for transportation and power generation because of their high fuel efficiency. However,...
Show moreCompression ignited diesel engines are widely used for transportation and power generation because of their high fuel efficiency. However, diesel engines can cause concerning environmental pollution because of their high nitrogen oxide (NOx) and soot emissions. In addition to meeting the stringent emission regulations, the demand to reduce greenhouse gas emissions has become urgent due to the more frequent destructive catastrophes caused by global warming in recent decades. In an effort to reduce emissions and improve fuel economy, many techniques have been developed and investigated by researchers. Air handling systems like exhaust gas recirculation and variable geometry turbochargers are the most widely used techniques on the market for modern diesel engines. Meanwhile, the concept of low temperature combustion is widely investigated by researchers. Low temperature combustion can increase the portion of pre-mixed fuel-air combustion to reduce the peak in-cylinder temperature so that the formation of NOx can be suppressed. Furthermore, the combustion characteristics and performance of bio-derived fuel blends are also studied to reduce overall greenhouse gas emissions through the reduced usage of fossil fuels. All the above mentioned systems are complicated because they involve not only chemical reactions but also complex fluid motion and mixing processes. As such, the control of these systems is always challenging and limits their commercial application. Currentlymost control methods are feed-forward control based on load condition and engine speed due to the simplicity in real-time application. With the development of faster control unit and deep learning techniques, the application of more complex control algorithms is possible to further improve the emissions and fuel economy. This work focuses on improvements to the control of engine air handling systems and combustion processes that leverage alternative fuels.Complex air handling systems, featuring technologies such as exhaust gas recirculation (EGR) and variable geometry turbochargers (VGTs), are commonly used in modern diesel engines to meet stringent emissions and fuel economy requirements. The control of diesel air handling systems with EGR and VGTs is challenging because of their nonlinearity and coupled dynamics. In this thesis, artificial neural networks (ANNs) and recurrent neural networks (RNNs) are applied to control the low pressure (LP) EGR valve position and VGT vane position simultaneously on a light-duty multi-cylinder diesel engine. In addition, experimental examination of a low temperature combustion based on gasoline compression ignition as well as its control has also been studied in this work. This type of combustion has been explored on traditional diesel engines in order to meet increasingly stringent emission regulations without sacrificing efficiency. In this study, a six-cylinder heavy-duty diesel engine was operated in a mixing controlled gasoline compression ignition mode to investigatethe influence of fuels and injection strategies on the combustion characteristics, emissions, and thermal efficiencies. Fuels, including ethanol (E), isobutanol (IB), and diisobutylene (DIB), were blended with a gasoline fuel to form E10, E30, IB30, and DIB30 based on volumetric fraction. These four blends along with gasoline formed the five test fuels. With these fuels, three injections strategies were investigated, including late pilot injection, early pilot injection, and port fuel injection/direct injection. The impact of moderate exhaust gas recirculation on nitrogen oxides and soot emissions was examined to determine the most promising fuel/injection strategy for emissions reduction. In addition, first and second law analyses were performed to provide insights into the efficiency, loss, and exergy destruction of the various gasoline fuel blends at low and medium load conditions. Overall, the emission output, thermal efficiency, and combustion performances of the five fuels were found to be similar and their differences are modest under most test conditions.While experimental work showed that low temperature combustion with alternative fuels could be effective, control is still challenging due to not only the properties of different gasoline-type fuels but also the impacts of injection strategies on the in-cylinder reactivity. As such, a computationally efficient zero-dimension combustion model can significantly reduce the cost of control development. In this study, a previously developed zero-dimension combustion model for gasoline compression ignition was extended to multiple gasoline-type fuel blends and a port fuel injection/direct fuel injection strategy. Tests were conducted on a 12.4-liter heavy-duty engine with five fuel blends. A modification was made to the functional ignition delay model to cover the significantly different ignition delay behavior between conventional and oxygenated fuel blends. The parameters in the model were calibrated with only gasoline data at a load of 14 bar brake mean effective pressure. The results showed that this physics-based model can be applied to the other four fuel blends at three differentpilot injection strategies without recalibration. In order to also facilitate the control of emissions, machine learning models were investigated to capture NOx emissions. A kernel-based extreme learning machine (K-ELM) performed best and had a coefficient of correlation (R-squared) of 0.998. The combustion and NOx emission models are valid for not only conventional gasoline fuel but also oxygenated alternative fuel blends at three different pilot injection strategies. In order to track key combustion metrics while keeping noise and emissions within constraints, a model predictive control(MPC) was applied for a compression ignition engine operating with a range of potential fuels and fuel injection strategies. The MPC is validated under different scenarios, including a load step change, fuel type change, and injection strategy change, with proportional-integral (PI) control as the baseline. The simulation results show that MPC can optimize the overall performance through modifying the main injection timing, pilot fuel mass, and exhaust gas recirculation (EGR) fraction.
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Title: Video Object Detection using CenterNet
Creator: Mondal, Madhusree
Date: 2021
Description: This thesis investigates the options of video object detection with key-point-based approaches. The problem of recognizing, locating, and...
Show moreThis thesis investigates the options of video object detection with key-point-based approaches. The problem of recognizing, locating, and tracking objects in videos has been a challenging task in the computer vision area. There are few applications on key-point-based object detectors like CornerNet and CenterNet. At the first stage, this work involves the use of the previously proposed CenterNet module as a baseline detector on each frame of the Imagenet Video dataset. Then we apply an RNN module to exploit the temporal information from the past frames for better results.There are challenges in video object detection compared to still image-based object detection. It is not efficient to apply a still-image-based detector on each frame independently because we cannot exploit the temporal contextual information in videos since neighboring frames in a video are highly correlated. Object detection from videos suffers from motion blur, video focus, rare poses, etc. To overcome these issues one way of improving CenterNet for video object detection is to propagate the previous reliable detection results to boost the detection performance.
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Title: TWO ESSAYS IN SUSTAINABILITY AND ASSET RETURN PREDICTABILITY
Creator: Nguyen, Lanh Vu Thuc
Date: 2021
Description: Our paper consists of two chapters in Financial Modeling for Sustainability and Asset Return Predictability. Recent developments in data...
Show moreOur paper consists of two chapters in Financial Modeling for Sustainability and Asset Return Predictability. Recent developments in data scraping and analytical methods have enhanced the possibility to construct the data and modeling required to examine the topics in each chapter. Chapter 1 proposes a simple yet strategic model involving a personal financial system to achieve a sustainable and prosperous future. The proposed model emphasizes the optimization of carbon footprints of one person at a time through the decentralization of the electricity use. While describing steps to develop a decentralized system considering electricity as a credit product, the model also underlines the importance of geographic economic dimensions and energy market prices due to their anticipated impact on the effectiveness of designing strategies for optimizing individuals’ energy use habits. Geographical conditions as well as market electricity prices can be used to signal individual energy use scores over time, therefore could also be instrumental in customizing energy use habits as the users realize variations in their energy use scores resulting from hourly electricity price changes at their locations. In other words, not only the changes in the individual’s behavior, but also the changes in the geographical conditions and community of users will affect the improvement of energy use behaviors of an individual over time using our model. We believe that the proposed model can be efficiently adopted to take on challenges threatening the future sustainability. While describing the basic characteristics of the model, we also open the possibility for future studies its capabilities to reduce carbon footprints from other societal choices, for example, using water, managing waste, or designing sustainable transportation systems. In Chapter 2, we examine asset return predictability, which is an important topic in finance with rich literature. Much of the current literature considers dividend yield as the main predictor for expected returns, and the main discussion centers around confirming or rejecting the predictive power of dividend yield with mixed evidence. However, dividend payments have been consistently declining and public firms have been increasingly using stock repurchase as the alternative to return values to shareholders. We aim to contribute to the literature by investigating a panel data of total equity payout, which takes into account not only dividend payout but also other forms of payment such as stock repurchase, as the main predictor for expected returns. In the asset return predictability literature, existing studies gather stock repurchase data from financial statements. In this paper, we manually construct our database of returns and payouts of public companies from various sources to create precise firm-level total equity payout dataset without relying on approximations from annual financial statements. This study adds to understanding of total equity payout and stock returns by analyzing a finer granularity than an annum and cross section of stock returns.
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